Business Intelligence SIG: Joint Meeting with "R" User Group (BARUG) and Predictive Analytics World

Description

This month we are joining with "R" User Group (BARUG) and Predictive Analytics World in San Francisco. For this event only, you MUST preregister here as seats are limited.

Our PAW sponsors have invited BARUG and BI SIG members to attend the PAW networking reception which will be held in the Yerba Buena Ballroom of the Marriott Marquis Hotel from 5:30 to 7:00P immediately before BARUG meeting.

Our main speaker for the evening will be Bryan Lewis author of several R packages and chief data scientist at Paradigm4. Bryan will demonstrate the scidb package for R

Tess Nesbitt of Upstream will lead off with a lightning talk about time-to-event statistical models she builds with R to help customize marketing strategies.

BARUG and BI SIG members who would like to attend the Predictive Analytics World conference can receive a 15% discount on the conference pass by using code BARUGSF13 when they register.

SciDB is a new, open-source database that organizes data in n-dimensional arrays. Along with a traditional database interface, the scidb package defines a sparse n-dimensional array class similar to big matrices defined by the bigmemory package. Interesting SciDB features include parallel processing, distributed storage, ACID transactions, efficient sparse array storage, and native linear algebra operations. SciDB array objects mimic regular R arrays, but their data are distributed and managed by SciDB, and operations on them are computed in parallel.

Bryan will illustrate using SciDB arrays in R with a few examples including computing a truncated singular value decomposition of a large matrix, and bi-clustering of large arrays using the biclust package.

Bryan Lewis has worked with R for several years and is the author of a number of R packages including irlba, rredis, doRedis, websockets, bigalgebra, and others. He is the chief data scientist at Paradigm4 inWaltham, MA and has a Ph.D. in Applied Mathematics.

Tess Nesbitt received a PhD in Statistics from UCLA, where where she stayed to teach in the Statistics Department for a year after graduation. Since transitioning from academia to Upstream, her most recent work centers around Time-to-event modeling with big data to quantify the relative influence of marketing campaigns.
She has also also done Experimental Design and Analysis, Lead Scoring, Fraud Detection, Clustering, and Broad Range of Predictive and Descriptive Modeling.